EE104: Introduction to Machine Learning

Stanford University, Spring Quarter 2018

The schedule below is tentative and will be updated (frequently) as we progress through the quarter.

In the table below, VMLS refers to the EE103 textbook, Introduction to Applied Linear Algebra - Vectors, Matrices, and Least Squares.

Date Slides Additional reading
4/3 introduction, supervised learning VMLS, chapters 12 and section 13.1.
4/5 linear regression, validation VMLS, section 13.1.
4/10 validation VMLS, section 13.2.
4/12 features VMLS, section 13.3.
4/17 features VMLS, section 13.3.
4/19 regularization and house prices example VMLS, section 15.4.
4/24 non-quadratic losses
4/26 non-quadratic regularizers
5/1 optimization
5/3 prox-gradient method
5/8 boolean classification VMLS, chapter 14.
5/10 multi-class classification VMLS, section 14.3.
5/15 multi-class classification VMLS, section 14.3.
5/17 neural networks
5/22 neural networks
5/24 unsupervised learning
5/29 principal component analysis
5/31 principal component analysis
6/5